Adaptive niche quantum-inspired immune clonal algorithm

• Published in: Natural computing Vol. 15; no. 2; pp. 297 - 305
• Main Authors: Liu, Jianyong, Wang, Huaixiao, Sun, Yangyang, Li, Ling
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.06.2016; Springer Nature B.V
• Subjects: Adaptive algorithms; Algorithms; Artificial Intelligence; Coding; Coding theory; Complex Systems; Computer Science; Convergence; Evolutionary Biology; Exploration; Mathematical analysis; Mathematical models; Optimization; Optimization algorithms; Processor Architectures; Quantum theory; Theory of Computation; Immune clonal algorithm; Niche; Optimization algorithm; Adaptive; Quantum-inspired evolutionary algorithm
• ISSN: 1567-7818; 1572-9796
• DOI: 10.1007/s11047-015-9495-4

The adaptive niche quantum-inspired immune clonal algorithm (ANQICA) is proposed by combining the quantum coding, immune clone and niche mechanism together to solve the multi-modal function optimization more effectively and make the function converge to as many as possible extreme value points. The quantum coding can better explore the solution space, the niche mechanism ensures the algorithm to converge to multi-extremum and the adaptive mechanism is introduced according to the characteristics of each procedure of the algorithm to improve the effect of the algorithm. Example analysis shows that the ANQICA is better in exploration and convergence. Therefore, the ANQICA can be used to solve the problem of multi-modal function optimization effectively.